Abstract
A constant false alarm rate (CFAR) target detector in non-homogenous backgrounds is proposed. Based on K-sample Anderson-Darling (AD) tests, the method re-arranges the reference cells by merging homogenous sub-blocks surrounding the cell under test (CUT) into a new reference window to estimate the background statistics. Double partition test, clutter edge refinement and outlier elimination are used as an anti-clutter processor in the proposed Modified AD (MAD) detector. Simulation results show that the proposed MAD test based detector outperforms cell-averaging (CA) CFAR, greatest of (GO) CFAR, smallest of (SO) CFAR, order-statistic (OS) CFAR, variability index (VI) CFAR, and CUT inclusive (CI) CFAR in most non-homogenous situations.
Highlights
Most target Constant False Alarm Rate (CFAR) detection algorithms are designed for a particular family of clutter distribution models
We can conclude that using the two-time reference window partition, clutter edge refinement method help the proposed Modified AD (MAD)-constant false alarm rate (CFAR) detector outperforms the CUT inclusive (CI)-CFAR in Receiver Operation Characteristic (ROC) and have a good
A modified AD test-based (MAD) CFAR algorithm is proposed in this paper
Summary
Most target Constant False Alarm Rate (CFAR) detection algorithms are designed for a particular family of clutter distribution models. Echo data in real environments are usually non-homogeneous and do not follow the assumed probability distribution model independent of which remote sensors are used such as radar, sonar, or chemical-detection sensors [1,2] This is due to the Sensors 2014, 14 multi-source detection environment, which degrades detection performance especially in multi-target and clutter edge backgrounds [3]. The earliest CFAR detector, Cell Average (CA)-CFAR [4], is optimal in a homogeneous background when the reference cells follow an independent and identical distribution (IID) to the cell under test (CUT) by an exponential distribution It does, suffer a serious performance degradation in multi-target and clutter edge backgrounds [5].
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